American Statistical Association
Biomarkers are often measured repeatedly in biomedical studies to help understand the development of diseases. In the Genetic and Inflammatory Markers of Sepsis (GenIMS) study, many markers on different pathways were measured for patients with community-acquired pneumonia during the course of hospitalization. However, the longitudinal analysis of these markers is complicated by the informative drop-outs due to death or discharge early, and left censoring due to detection limits of the given assays. To account for these two issues, we consider a weighting technique for quantile regression models, which impose minimal assumptions on the distribution of the data. In particular, we weight the estimating equation for censored quantile regression by the inverse probability of drop-out. We evaluate our method through simulation studies and use a GenIMS data set for demonstration.
Lan Kong is an assistant professor at the Department of Biostatistics, University of Pittsburgh. She obtained her Ph.D. degree in Biostatistics from the University of North Carolina at Chapel Hill. Her research interests focus on survival modeling for time to event data arising from case-cohort studies, risk prediction with multiple markers measured over time and statistical methods for analyzing censored data due to detection limits. She collaborates with investigators at the Department of Critical Care Medicine on studies of acute illness such as sepsis, septic shock and acute kidney injury.
|Date:||Thursday, March 11, 2010|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York